Clustering of rainfall stations and distinguishing influential factors using PCA and HCA techniques over the western dry region of India

Abstract

This study used hierarchical cluster analysis (HCA) to delineate the spatial patterns of monthly, seasonal and annual rainfall by clustering 62 stations in the western arid region of India based on a 55 year data set. The statistical properties of clusters were computed and box-whisker plots plotted. Furthermore, the relative influence of three geographical factors (longitude, latitude and altitude) and five statistical parameters (the mean, standard deviation (SD), co-efficient of variation (CV), and maximum and minimum rainfall) on mean rainfall was investigated using principal component analysis (PCA). The use of HCA resulted in four rainfall clusters geographically located at a distinct position. Cluster I, characterized by the lowest mean rainfall and highest CV, was located in the western portion, whereas mean rainfall was the highest for cluster IV situated in the eastern portion. Box-whisker plots revealed a slight skewness, although the monsoon and annual rainfall followed a normal distribution. The PCA results indicted two to three significant principal components (PCs) with eigenvalues > 1. In four clusters, two PCs explained the major variance, ranging from 69.41% (June) to 91.83% (August) in monthly rainfall, from 63.62% (monsoon) to 93.30% (post-monsoon) in seasonal rainfall, and from 71.48% to 90.73% in annual rainfall. In monthly and seasonal rainfall, first PC 1 is termed the "mean rainfall component", which has strong to moderate associations with longitude, and is equally opposed by the CV. These findings are vital for planners and decision-makers to formulate strategies to manage unusual rainwater quantities.

Regionalization and Trend Analysis of Seasonal Precipitation (1970 - 2020) using Hierarchical Clustering and Wavelet Transform Approach in the Mahi River Basin, India

“…Out of all these methods, for cluster analysis, HCA is the most suitable method to identify meteorological homogeneous areas in the regions by grouping historical rain gauges datasets and points (Hu et al 2019;Alam & Paul 2020). Recently, Machiwal et al 2019 investigated comparative analyses to find out the homogeneous rainfall gauge positions using a clustering algorithm. The potential effects due to climatic change may alter the pattern of precipitation and variability (Samra 2004).…”

Importance of regional rainfall data in homogeneous clustering of data-sparse areas: a study in the upper Brahmaputra valley region

“…Regionalization with the use of principal component analysis (PCA) was found to be of use (Singh and Singh 1996;Wotling et al 2000). When subjectivity involved with PCA came into notice, the concept of cluster analysis started getting attention (Bonell and Sumner 1992;Guttman 1993;Venkatesh and Jose 2007;Machiwal et al 2019). Cluster analysis refers to a varied group of statistical procedures used to classify a multivariate dataset into some clusters or groups (Rao and Srinivas 2006a, b;Srinivas et al 2008;Dikbas et al 2012).…”

“…Cluster analysis refers to a varied group of statistical procedures used to classify a multivariate dataset into some clusters or groups (Rao and Srinivas 2006a, b;Srinivas et al 2008;Dikbas et al 2012). Studies have also been done where PCA was further associated with various cluster analysis techniques for homogeneous clustering (Dinpashoh et al 2004;Satyanarayana and Srinivas 2011;Darand and Daneshvar 2014;Machiwal et al 2019). Ward's hierarchical cluster analysis is one of the widely used methods, that is found to be suitable for homogeneous regionalization (Unal et al 2003;Baltacı et al 2017).…”

A Regional Approach for Investigation of Temporal Precipitation Changes

“…There are many approaches in the literature for the investigation of the spatial pattern of rainfall, relying on mathematical transformations, statistical regressions, or clustering techniques, such as multivariate regression, spatial interpolation, spatial correlation, harmonic analysis, L-moments, principal component analysis, hierarchical and non-hierarchical clustering techniques [17][18][19]. These approaches use basic basin physiographic and climatic properties for defining the rainfall pattern in a considered area and have been studied by many researchers to obtain a robust methodology that can be applied within a defined homogenous region [20][21][22][23].…”